CUDA 4.1 makes it easier to accelerate scientific research with GPUs with key features including

• a redesigned Visual Profiler with automated performance analysis and expert guidance;
• a new LLVM-based compiler that generates up to 10% faster code; and
• 1000+ new imaging and signal processing functions in the NPP library.

The CuSparse library included with CUDA 4.1 has a new tridiagonal solver and 2x faster sparse matrix-vector multiplication using the ELL hybrid format, and the CuRand library included with CUDA 4.1 has two new random number generators.  The CUDA 4.1 toolkit also brings some great improvements to its debugging and performance analysis tools.

Sign up for a webinar to learn more about all the new features & high performance GPU-accelerated libraries!

CUDA 4.1 Toolkit 4.1 Feature Overview Webinar

• For Europe and The Americas: 10am (PST), Wednesday, Feb 1
• For Asia-Pacific and India:  10am (IST) Friday, Feb 3

ispc is a new compiler for “single program, multiple data” (SPMD) programs; the same model that is used for (GP)GPU programming, but here targeted to CPUs. ispc compiles a C-based SPMD programming language to run on the SIMD units of CPUs; it frequently provides a a 3x or more speedup on CPUs with 4-wide SSE units, without any of the difficulty of writing intrinsics code. There were a few principles and goals behind the design of ispc:

• To build a small C-like language that would deliver excellent performance to performance-oriented programmers who want to run SPMD programs on the CPU.
• To provide a thin abstraction layer between the programmer and the hardware—in particular, to have an execution and data model where the programmer can cleanly reason about the mapping of their source program to compiled assembly language and the underlying hardware.
• To make it possible to harness the computational power of the SIMD vector units without the extremely low-programmer-productivity activity of directly writing intrinsics.
• To explore opportunities from close coupling between C/C++ application code and SPMD ispc code running on the same processor—to have lightweight function calls between the two languages, to share data directly via pointers without copying or reformatting, and so forth.

ispc is an open source compiler with a BSD license. It uses the LLVM Compiler Infrastructure for back-end code generation and optimization and is hosted on github. It supports Windows, Mac, and Linux, with both x86 and x86-64 targets. It currently supports the SSE2 and SSE4 instruction sets, though support for AVX should be available soon.

• Basic CUDA Driver and Runtime API support on CUDA 4.0rc2 with unit tests.
• Object-Oriented API.
• Exception classes for CUDA errors.
• Support for Linux and Mac OSX platforms.
• Documented with YARD.

See http://blog.speedgocomputing.com/2011/04/first-release-of-sgc-ruby-cuda.html for more details.

SANTA CLARA, CA — (Marketwire) — 02/28/2011 – NVIDIA today announced the latest version of the NVIDIA® CUDA® Toolkit for developing parallel applications using NVIDIA GPUs.

The NVIDIA CUDA 4.0 Toolkit was designed to make parallel programming easier, and enable more developers to port their applications to GPUs. This has resulted in three main features:

• NVIDIA GPUDirect™ 2.0 Technology – Offers support for peer-to-peer communication among GPUs within a single server or workstation. This enables easier and faster multi-GPU programming and application performance.
• Unified Virtual Addressing (UVA) – Provides a single merged-memory address space for the main system memory and the GPU memories, enabling quicker and easier parallel programming.
• Thrust C++ Template Performance Primitives Libraries – Provides a collection of powerful open source C++ parallel algorithms and data structures that ease programming for C++ developers. With Thrust, routines such as parallel sorting are 5X to 100X faster than with Standard Template Library (STL) and Threading Building Blocks (TBB).

“Unified virtual addressing and faster GPU-to-GPU communication makes it easier for developers to take advantage of the parallel computing capability of GPUs,” said John Stone, senior research programmer, University of Illinois, Urbana-Champaign.

“Having access to GPU computing through the standard template interface greatly increases productivity for a wide range of tasks, from simple cashflow generation to complex computations with Libor market models, variable annuities or CVA adjustments,” said Peter Decrem, director of Rates Products at Quantifi. ”The Thrust C++ library has lowered the barrier of entry significantly by taking care of low-level functionality like memory access and allocation, allowing the financial engineer to focus on algorithm development in a GPU-enhanced environment.”

The CUDA 4.0 architecture release includes a number of other key features and capabilities, including:

• MPI Integration with CUDA Applications – Modified MPI implementations automatically move data from and to the GPU memory over Infiniband when an application does an MPI send or receive call.
• Multi-thread Sharing of GPUs – Multiple CPU host threads can share contexts on a single GPU, making it easier to share a single GPU by multi-threaded applications.
• Multi-GPU Sharing by Single CPU Thread – A single CPU host thread can access all GPUs in a system. Developers can easily coordinate work across multiple GPUs for tasks such as “halo” exchange in applications.
• New NPP Image and Computer Vision Library – A rich set of image transformation operations that enable rapid development of imaging and computer vision applications.
• New and Improved Capabilities
  • Auto performance analysis in the Visual Profiler
  • New features in cuda-gdb and added support for MacOS
  • Added support for C++ features like new/delete and virtual functions
  • New GPU binary disassembler

A release candidate of CUDA Toolkit 4.0 will be available free of charge beginning March 4, 2011, by enrolling in the CUDA Registered Developer Program at: www.nvidia.com/paralleldeveloper. The CUDA Registered Developer Program provides a wealth of tools, resources, and information for parallel application developers to maximize the potential of CUDA.

For more information on the features and capabilities of the CUDA Toolkit and on GPGPU applications, please visit:www.nvidia.com/cuda.

VCL can be used to build powerful parallel GPU based clusters. Its main features are:

• Can run unmodified OpenCL 1.1 applications
• Applications can utilize cluster-wide GPU devices
• Transparent selection of devices
• Applications can be started on any hosting-computer, including workstations without GPU devices
• Supports multiple applications on the same cluster
• Runs on Linux clusters, with or without MOSIX

The VCL package can be downloaded from http://www.mosix.org/txt_vcl.html.

CLyther currently only supports a subset of the Python language definition but adds many new features to OpenCL such as:

• OpenCL interface similar to PyOpenCL
• Dynamic compilation of OpenCL code at runtime
• Fast prototyping of OpenCL code
• Create OpenCL code using the Python language definition
• Passing functions as arguments to OpenCL kernels
• Pure Python emulation mode of kernel functions

Future features will include the following.

• Dynamic creation of Device Objects.
• Create and define objects in Python to pass to kernel functions.
• Support for more python built-in functions and types.
• Support for Python iterators.
• Fancy indexing of arrays.
• CLyther compiler to generate pure C/C++ code